

'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]						)				->  Dict:
						lowercase :							Dict    		=SwinvaConfig()
						lowercase :							str    		=swinva_name.split('''_'''						)

						lowercase :							Dict    		=name_split[1]
						if "to" in name_split[3]:
												lowercase :							Optional[Any]    		=int(name_split[3][-3:]						)
						else:
												lowercase :							Tuple    		=int(name_split[3]						)
						if "to" in name_split[2]:
												lowercase :							Optional[int]    		=int(name_split[2][-2:]						)
						else:
												lowercase :							Union[str, Any]    		=int(name_split[2][6:]						)

						if model_size == "tiny":
												lowercase :							Tuple    		=96
												lowercase :							Any    		=(2, 2, 6, 2)
												lowercase :							Union[str, Any]    		=(3, 6, 12, 24)
						elif model_size == "small":
												lowercase :							List[str]    		=96
												lowercase :							Optional[Any]    		=(2, 2, 18, 2)
												lowercase :							Optional[Any]    		=(3, 6, 12, 24)
						elif model_size == "base":
												lowercase :							str    		=128
												lowercase :							Dict    		=(2, 2, 18, 2)
												lowercase :							Optional[Any]    		=(4, 8, 16, 32)
						else:
												lowercase :							Optional[int]    		=192
												lowercase :							Dict    		=(2, 2, 18, 2)
												lowercase :							Any    		=(6, 12, 24, 48)

						if "to" in swinva_name:
												lowercase :							Any    		=(12, 12, 12, 6)

						if ("22k" in swinva_name) and ("to" not in swinva_name):
												lowercase :							Optional[int]    		=21841
												lowercase :							List[str]    		='''huggingface/label-files'''
												lowercase :							int    		='''imagenet-22k-id2label.json'''
												lowercase :							int    		=json.load(open(hf_hub_download(__magic_name__   , __magic_name__   , repo_type='''dataset'''						)   , '''r'''						)						)
												lowercase :							Any    		={int(__magic_name__						): v for k, v in idalabel.items()}
												lowercase :							Dict    		=idalabel
												lowercase :							Dict    		={v: k for k, v in idalabel.items()}

						else:
												lowercase :							Dict    		=1000
												lowercase :							Optional[Any]    		='''huggingface/label-files'''
												lowercase :							Optional[Any]    		='''imagenet-1k-id2label.json'''
												lowercase :							str    		=json.load(open(hf_hub_download(__magic_name__   , __magic_name__   , repo_type='''dataset'''						)   , '''r'''						)						)
												lowercase :							int    		={int(__magic_name__						): v for k, v in idalabel.items()}
												lowercase :							Any    		=idalabel
												lowercase :							Dict    		={v: k for k, v in idalabel.items()}

						lowercase :							Union[str, Any]    		=img_size
						lowercase :							List[Any]    		=num_classes
						lowercase :							str    		=embed_dim
						lowercase :							int    		=depths
						lowercase :							Optional[Any]    		=num_heads
						lowercase :							List[str]    		=window_size

						return config






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]						)				->  List[Any]:
						if "patch_embed.proj" in name:
												lowercase :							List[Any]    		=name.replace('''patch_embed.proj'''   , '''embeddings.patch_embeddings.projection'''						)
						if "patch_embed.norm" in name:
												lowercase :							Union[str, Any]    		=name.replace('''patch_embed.norm'''   , '''embeddings.norm'''						)
						if "layers" in name:
												lowercase :							Optional[Any]    		='''encoder.''' + name
						if "attn.proj" in name:
												lowercase :							List[str]    		=name.replace('''attn.proj'''   , '''attention.output.dense'''						)
						if "attn" in name:
												lowercase :							Optional[Any]    		=name.replace('''attn'''   , '''attention.self'''						)
						if "norm1" in name:
												lowercase :							Any    		=name.replace('''norm1'''   , '''layernorm_before'''						)
						if "norm2" in name:
												lowercase :							str    		=name.replace('''norm2'''   , '''layernorm_after'''						)
						if "mlp.fc1" in name:
												lowercase :							Any    		=name.replace('''mlp.fc1'''   , '''intermediate.dense'''						)
						if "mlp.fc2" in name:
												lowercase :							int    		=name.replace('''mlp.fc2'''   , '''output.dense'''						)
						if "q_bias" in name:
												lowercase :							Union[str, Any]    		=name.replace('''q_bias'''   , '''query.bias'''						)
						if "k_bias" in name:
												lowercase :							Optional[int]    		=name.replace('''k_bias'''   , '''key.bias'''						)
						if "v_bias" in name:
												lowercase :							Optional[int]    		=name.replace('''v_bias'''   , '''value.bias'''						)
						if "cpb_mlp" in name:
												lowercase :							Any    		=name.replace('''cpb_mlp'''   , '''continuous_position_bias_mlp'''						)
						if name == "norm.weight":
												lowercase :							Tuple    		='''layernorm.weight'''
						if name == "norm.bias":
												lowercase :							int    		='''layernorm.bias'''

						if "head" in name:
												lowercase :							Any    		=name.replace('''head'''   , '''classifier'''						)
						else:
												lowercase :							int    		='''swinv2.''' + name

						return name






def 				_lowerCAmelCase			(   __magic_name__						:     Union[str, Any]   , __magic_name__						:     Optional[int]						)				->  Union[str, Any]:
						for key in orig_state_dict.copy().keys():
												lowercase :							int    		=orig_state_dict.pop(__magic_name__						)

												if "mask" in key:
																		continue
												elif "qkv" in key:
																		lowercase :							Optional[int]    		=key.split('''.'''						)
																		lowercase :							Optional[int]    		=int(key_split[1]						)
																		lowercase :							List[str]    		=int(key_split[3]						)
																		lowercase :							Union[str, Any]    		=model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								lowercase :							Any    		=val[:dim, :]
																								lowercase :							Any    		=val[dim : dim * 2, :]
																								lowercase :							Any    		=val[-dim:, :]
																		else:
																								lowercase :							List[str]    		=val[:dim]
																								lowercase :							Any    		=val[
																								    dim : dim * 2
																								]
																								lowercase :							str    		=val[-dim:]
												else:
																		lowercase :							Tuple    		=val

						return orig_state_dict






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  List[str]:
						lowercase :							Tuple    		=timm.create_model(__magic_name__   , pretrained=__magic_name__						)
						timm_model.eval()

						lowercase :							int    		=get_swinva_config(__magic_name__						)
						lowercase :							List[Any]    		=SwinvaForImageClassification(__magic_name__						)
						model.eval()

						lowercase :							int    		=convert_state_dict(timm_model.state_dict()   , __magic_name__						)
						model.load_state_dict(__magic_name__						)

						lowercase :							Tuple    		='''http://images.cocodataset.org/val2017/000000039769.jpg'''

						lowercase :							Optional[int]    		=AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''   , '''-'''						)						)						)
						lowercase :							Tuple    		=Image.open(requests.get(__magic_name__   , stream=__magic_name__						).raw						)
						lowercase :							str    		=image_processor(images=__magic_name__   , return_tensors='''pt'''						)

						lowercase :							Any    		=timm_model(inputs['''pixel_values''']						)
						lowercase :							Optional[int]    		=model(**__magic_name__						).logits

						assert torch.allclose(__magic_name__   , __magic_name__   , atol=1E-3						)

						print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''						)
						model.save_pretrained(__magic_name__						)

						print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
						image_processor.save_pretrained(__magic_name__						)

						model.push_to_hub(
						    repo_path_or_name=Path(__magic_name__   , __magic_name__						)   , organization='''nandwalritik'''   , commit_message='''Add model'''   , )


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--swinv2_name""",
							    default="""swinv2_tiny_patch4_window8_256""",
							    type=str,
							    help="""Name of the Swinv2 timm model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)

							UpperCamelCase_    						=       parser.parse_args()
							convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=99  ,      _lowercase=13  ,      _lowercase=16  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=2  ,      _lowercase=32  ,      _lowercase=4  ,      _lowercase=4  ,      _lowercase=30  ,      _lowercase=0  ,      _lowercase=1  ,      _lowercase=2  ,      _lowercase=None  ,      )     -> List[str]:
									a_						:						Union[str, Any] 							= parent
									a_						:						Dict 							= batch_size
									a_						:						Any 							= decoder_seq_length
									# For common tests
									a_						:						Any 							= self.decoder_seq_length
									a_						:						int 							= is_training
									a_						:						List[Any] 							= use_attention_mask
									a_						:						List[str] 							= use_labels

									a_						:						Optional[Any] 							= vocab_size
									a_						:						Dict 							= d_model
									a_						:						Any 							= d_model
									a_						:						List[Any] 							= decoder_layers
									a_						:						List[str] 							= decoder_layers
									a_						:						Union[str, Any] 							= decoder_ffn_dim
									a_						:						Optional[Any] 							= decoder_attention_heads
									a_						:						Optional[Any] 							= decoder_attention_heads
									a_						:						Tuple 							= eos_token_id
									a_						:						Union[str, Any] 							= bos_token_id
									a_						:						Dict 							= pad_token_id
									a_						:						Union[str, Any] 							= decoder_start_token_id
									a_						:						List[Any] 							= use_cache
									a_						:						List[str] 							= max_position_embeddings

									a_						:						int 							= None
									a_						:						Any 							= decoder_seq_length
									a_						:						Union[str, Any] 							= 2
									a_						:						Optional[int] 							= 1





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[int] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      self.vocab_size    )

									a_						:						Optional[int] 							= None
									if self.use_attention_mask:
												a_						:						List[Any] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      vocab_size=2    )

									a_						:						List[Any] 							= None
									if self.use_labels:
												a_						:						Union[str, Any] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      self.vocab_size    )

									a_						:						Any 							= TrOCRConfig(
									    vocab_size=self.vocab_size  ,      d_model=self.d_model  ,      decoder_layers=self.decoder_layers  ,      decoder_ffn_dim=self.decoder_ffn_dim  ,      decoder_attention_heads=self.decoder_attention_heads  ,      eos_token_id=self.eos_token_id  ,      bos_token_id=self.bos_token_id  ,      use_cache=self.use_cache  ,      pad_token_id=self.pad_token_id  ,      decoder_start_token_id=self.decoder_start_token_id  ,      max_position_embeddings=self.max_position_embeddings  ,      )

									return (config, input_ids, attention_mask, lm_labels)





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> List[Any]:
									a_						:						Optional[int] 							= True
									a_						:						List[str] 							= TrOCRDecoder(config=_lowercase    ).to(_lowercase    ).eval()
									a_						:						str 							= input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									a_						:						Dict 							= model(_lowercase  ,      use_cache=_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase  ,      use_cache=_lowercase    )

									self.parent.assertTrue(len(_lowercase    ) == len(_lowercase    )    )
									self.parent.assertTrue(len(_lowercase    ) == len(_lowercase    ) + 1    )

									a_						:						Dict 							= outputs["""past_key_values"""]

									# create hypothetical next token and extent to next_input_ids
									a_						:						List[str] 							= ids_tensor((2, 1)  ,      config.vocab_size - 1    ) + 1

									# append to next input_ids and
									a_						:						int 							= torch.cat([input_ids, next_tokens]  ,      dim=-1    )

									a_						:						Dict 							= model(_lowercase    )["""last_hidden_state"""]
									a_						:						List[str] 							= model(_lowercase  ,      past_key_values=_lowercase    )["""last_hidden_state"""]

									# select random slice
									a_						:						Optional[Any] 							= ids_tensor((1,)  ,      output_from_past.shape[-1]    ).item()
									a_						:						Union[str, Any] 							= output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									a_						:						Any 							= output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						List[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						List[Any] 							= config_and_inputs

									a_						:						str 							= {"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict







@require_torch
class        A__(a_,  a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						_A    :				Optional[int]       					=			(TrOCRForCausalLM,) if is_torch_available() else ()
						_A    :				Tuple       					=			{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
						_A    :				List[str]       					=			True
						_A    :				List[str]       					=			False





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						str 							= TrOCRStandaloneDecoderModelTester(self  ,      is_training=_lowercase    )
									a_						:						str 							= ConfigTester(self  ,      config_class=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									pass





						def 			UpperCamelCase__					(    self    )     -> int:
									pass





						def 			UpperCamelCase__					(    self    )     -> str:
									pass





						def 			UpperCamelCase__					(    self    )     -> Dict:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						int 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									return





						@unittest.skip("""The model doesn't support left padding"""    )  # and it's not used enough to be worth fixing :)
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									pass


